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基于脆弱性分析的复杂产品生产线工艺路线优化 被引量:3
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作者 李华 卢素伟 杨挺 《中国机械工程》 EI CAS CSCD 北大核心 2014年第23期3168-3173,共6页
复杂产品的制造加工具有工艺路线复杂、制造工序众多的特点,产品生产线往往具有高的脆弱性。应用复杂网络理论建立了复杂产品的生产线网络模型,并通过对复杂产品生产线网络模型特点进行分析,揭示了工序在生产线网络中分布的不均衡性会... 复杂产品的制造加工具有工艺路线复杂、制造工序众多的特点,产品生产线往往具有高的脆弱性。应用复杂网络理论建立了复杂产品的生产线网络模型,并通过对复杂产品生产线网络模型特点进行分析,揭示了工序在生产线网络中分布的不均衡性会导致生产线脆弱的本质,进而建立了工序分布偏差函数,构建了复杂产品生产线的网络优化模型,通过优化工艺路线提高了工序分布的均衡性。以某航空发动机零部件生产线为例验证了方法的可行性和有效性。 展开更多
关键词 复杂产品 生产线网络模型 工艺路线优化 脆弱性
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Estimating Average Reservoir Pressure: A Neural Network Approach with Limited Data
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作者 Saber Elmabrouk Ezeddin Shirit Rene Mayouga 《Journal of Earth Science and Engineering》 2012年第11期663-675,共13页
Insight into average oil pressure in gas reservoirs and changes in production (time), play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, ... Insight into average oil pressure in gas reservoirs and changes in production (time), play a critical role in reservoir and production performance, economic evaluation and reservoir management. In all practicality, average reservoir pressure can be conducted only when producing wells are shut in. This is regarded as a pressure build-up test. During the test, the wellbore pressure is recorded as a function of time. Currently, the only available method with which to obtain average reservoir pressure is to conduct an extended build-up test. It must then be evaluated using Homer or MDH (Miller, Dyes and Huchinson) valuation procedures. During production, average reservoir pressure declines due to fluid withdrawal from the wells and therefore, the average reservoirpressure is updated, periodically. A significant economic loss occurs during the entire pressure build-up test when producing wells are shut in. In this study, a neural network model has been established to map a nonlinear time-varying relationship which controls reservoir production history in order to predict and interpolate average reservoir pressure without closing the producing wells. This technique is suitable for constant and variable flow rates. 展开更多
关键词 Artificial neural networks average reservoir pressure estimation modeling error analysis.
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